# pandas DataFrame describe for categorical and numeric data
# based on dtypes(object or numeric), check the summary info separately

import pandas as pd
import numpy as np


iris = pd.read_csv(r'H:\python\data\iris.csv')
# data info with each column dtype
iris.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 150 entries, 0 to 149
Data columns (total 5 columns):
sepal _length    150 non-null float64
sepal_width      150 non-null float64
petal_length     150 non-null float64
petal_width      150 non-null float64
iris_class       150 non-null object
dtypes: float64(4), object(1)
memory usage: 7.0+ KB

def check_qual(indata):
    return indata.select_dtypes(include = ['object']).describe()

def check_quant(indata):
    return indata.select_dtypes(exclude = ['object']).describe()


check_qual(iris)

check_quant(iris)